Use machine learning with anonymous patient data to predict the best drug to treat heart disease

Summary

DISCLAIMER: This notebook is used for demonstrative and illustrative purposes only and does not constitute an offering that has gone through regulatory review. It is not intended to serve as a medical application. There is no representation as to the accuracy of the output of this application and it is presented without warranty.

In this code pattern, we use anonymous patient data to predict the best medication to treat heart disease. This notebook introduces commands for getting data, building the model, model deployment, and scoring.

Description

Using machine learning in an application can produce impressive results, but moving from the model training stage to a production application is a lot of work. While frameworks like Apache Spark MLlib, scikit-learn, and Xgboost can help to reduce the model building workload, IBM Watson Machine Learning is a solution that can put those models into production in minutes. By taking advantage of Watson Machine Learning web service deployment of models, you can easily start building your application with powerful REST APIs.

In this code pattern, we use the machine learning classification algorithm to solve a requirement from a fictional biomedical company that produces heart medication. The company has collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of five medications. Based on treatment records, the company would like to predict the best medication for the patient. The pattern shows the exact steps demonstrating how that data and the Spark MLlib package are used to train a model that predicts the best medication.

Next, the trained model is published to a Watson Machine Learning repository on IBM Cloud and then deployed as a web service. The new patient’s records are sent in an authenticated request to the scoring endpoint, and the model returns a drug recommendation in the response.